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相关概念视频

Aliasing01:18

Aliasing

117
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
117
Upsampling01:22

Upsampling

195
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
195
Bandpass Sampling01:17

Bandpass Sampling

159
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
159
Active Filters01:25

Active Filters

710
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
710

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相关实验视频

Updated: May 31, 2025

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在RIS辅助的毫米波系统中用于通道估计的自适应过.

Shuying Shao1, Tiejun Lv1, Pingmu Huang2

  • 1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
概括

新的自适应算法改善了对具有可重新配置智能表面 (RIS) 的毫米波 (mmWave) 大规模MIMO系统的通道估计. 这些方法提高了准确性和速度,克服了RIS信号处理的局限性,以实现更好的无线通信.

关键词:
适应性过是一种自适应性过.道刺激 (CE) 是指道刺激 (CE) 是指道刺激.可重新配置的智能表面 (RIS)稀疏的毫米波系统

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科学领域:

  • 无线通信无线通信
  • 信号处理 信号处理
  • 信息理论 信息理论

背景情况:

  • 与可重新配置的智能表面 (RIS) 集成的毫米波 (mmWave) 大规模MIMO系统承诺增强无线通信.
  • 在这些系统中,由于RIS信号处理的局限性,通道估计 (CE) 面临挑战.
  • 现有的CE方法很难满足先进无线网络的需求.

研究的目的:

  • 为毫米波巨型MIMO-RIS系统提出适应性通道估计框架.
  • 开发新的算法,提高CE准确性和融合速度.
  • 为了减少这些先进系统中通道估计的计算复杂性.

主要方法:

  • 开发了两个自适应算法:Log-Sum规范最小平均平方 (Log-Sum NLMS) 和混合规范最小平均平方-规范最小平均第四 (混合NLMS-NLMF).
  • 利用毫米波频道的稀疏性来改善估计.
  • 在Log-Sum NLMS中包含了对Log-Sum NLMS进行更快的融合的逻辑总和惩罚,以及在混合NLMS-NLMF中用于各种SNR条件的混合错误函数.

主要成果:

  • 拟议的Log-Sum NLMS和混合NLMS-NLMF算法在道估计准确度方面取得了显著的改进.
  • 与NLMS,SEFWLMS和SHAFA等现有方法相比,这两种算法都表现出更快的融合速度.
  • 新的算法显示了较低的计算复杂性.

结论:

  • 拟议的自适应通道估计框架有效地解决了毫米波大规模MIMO-RIS系统中的CE挑战.
  • 逻辑总和NLMS和混合NLMS-NLMF在准确性和融合速度方面提供了卓越的性能.
  • 这些算法代表了未来无线通信技术的重大进步.